MLOps Your Way with the JFrog Platform

Just like in traditional software development, creating AI applications isn’t a one size fits all approach. However, many of the challenges and concerns facing AI/ML development teams share common threads – difficulties getting models to production, tangled infrastructure, data quality, security issues, and so on. Regardless of how you build it, to accelerate production-ready AI, …

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Feature Store Benefits: The Advantages of Feature Stores in Machine Learning Development

Feature stores are rapidly growing in popularity as organizations look to improve their machine learning productivity and operations (MLOps). With the advancements in MLOps, feature stores are becoming an essential component of the machine learning infrastructure, helping organizations to improve the performance and ability to explain their models, and accelerate the integration of new models …

Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain – Part 1

As businesses realized the potential of artificial intelligence (AI), the race began to incorporate machine learning operations (MLOps) into their commercial strategies. But the integration of machine learning (ML) into the real world proved challenging, and the vast gap between development and deployment was made clear. In fact, research from Gartner tells us 85% of …

Machine Learning Bug Bonanza – Exploiting ML Services

JFrog’s security research team continuously monitors open-source software registries, proactively identifying and addressing potential malware and vulnerability threats to foster a secure and reliable ecosystem for open-source software development and deployment. In our previous research on MLOps we noted the immaturity of the Machine Learning (ML) field often results in a higher amount of discovered …

swampUP Recap: “EveryOps” is Trending as a Software Development Requirement

swampUP 2024, the annual JFrog DevOps Conference, was unique in it’s addressing not only more familiar DevOps and DevSecOps issues, but adding specific operational challenges, stemming from the explosive growth of GenAI and the resulting need for specialized capabilities for handling AI models and datasets, while supporting new personae such as AI/ML engineers, data scientists …

Trusted Software Delivered!

At swampUP 2024 in Austin just a few days ago, we explored the EveryOps Matters approach with the crowd of developers, driven by a consolidated view from their companies’ boardrooms and 2024 CIO surveys. The message was clear: “EveryOps” isn’t just a strategy or tech trend —  it’s a fundamental, ongoing mindset shift that must …

From MLOps to MLOops: Exposing the Attack Surface of Machine Learning Platforms

From MLOps to MLOops: Exposing the Attack Surface of Machine Learning Platforms

NOTE: This research was recently presented at Black Hat USA 2024, under the title “From MLOps to MLOops – Exposing the Attack Surface of Machine Learning Platforms”. The JFrog Security Research team recently dedicated its efforts to exploring the various attacks that could be mounted on open source machine learning (MLOps) platforms used inside organizational …